These methods predict three probability signals for actionness (def REF ), startness and endness scores (def REF ). They generate temporal proposals by matching the temporal positions that are likely to be the start or end of an action (peak of startness and endness signals).
In BSN [1]} proposal features are constructed by concatenation of a fixed number of points, sampled from the actionness scores (def REF ) by linear interpolation. BSN ignores the global information for actions with blurred boundaries, causing unreliable confidence scores. Also, proposal features are too weak to capture enough temporal context, and the feature construction and confidence evaluation are performed for each proposal separately, which is inefficient. BMN [2]} explores the global context for simultaneously evaluating all proposals end-to-end. They construct a feature map by aggregating the features of all proposals together. The feature map is fed to convolution layers to simultaneously evaluate all proposals. The advantage of this approach is to extract rich feature and temporal context for each proposal and exploit the context of adjacent proposals. Also, proposal evaluation is very fast during inference. However, they use the same method as BSN [1]} to generate boundary probabilities (start and end) which ignores the global information for actions with blurred boundaries. DBG [4]} simultaneously evaluates all proposals to explore global context and extract rich features similar to BMN [2]}. Moreover, instead of only exploiting the local information to predict boundary probabilities (probability of start and end), DBG proposed to employ global proposal-level features.
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